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| Content Provider | Springer Nature Link |
|---|---|
| Author | Grassi, Marco Cambria, Erik Hussain, Amir Piazza, Francesco |
| Copyright Year | 2011 |
| Abstract | The recent success of media-sharing services caused an exponential growth of community-contributed multimedia data on the Web and hence a consistent shift of the flow of information from traditional communication channels to social media ones. Retrieving relevant information from this kind of data is getting more and more difficult, not only for their volume, but also for the different nature and formats of their contents. In this work, we introduce Sentic Web, a new paradigm for the management of social media affective information, which exploits AI and Semantic Web techniques to extract, encode, and represent opinions and sentiments over the Web. In particular, the computational layer consists in an intelligent engine for the inference of emotions from text, the representation layer is developed on the base of specific domain ontologies, and the application layer is based on the faceted browsing paradigm to make contents available as an interconnected knowledge base. |
| Starting Page | 480 |
| Ending Page | 489 |
| Page Count | 10 |
| File Format | |
| ISSN | 18669956 |
| Journal | Cognitive Computation |
| Volume Number | 3 |
| Issue Number | 3 |
| e-ISSN | 18669964 |
| Language | English |
| Publisher | Springer-Verlag |
| Publisher Date | 2011-05-21 |
| Publisher Place | New York |
| Access Restriction | One Nation One Subscription (ONOS) |
| Subject Keyword | Sentic computing AI Semantic web Ontologies NLP Emotion and affective UI Artificial Intelligence (incl. Robotics) Computation by Abstract Devices Neurosciences Computational Biology/Bioinformatics |
| Content Type | Text |
| Resource Type | Article |
| Subject | Cognitive Neuroscience Computer Science Applications Computer Vision and Pattern Recognition |
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